Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	144,444
2	Rhode Island	143,383
3	South Dakota	140,423
4	Utah	126,811
5	Tennessee	124,302
6	Arizona	121,225
7	Iowa	117,789
8	Wisconsin	115,922
9	Nebraska	115,505
10	South Carolina	115,276
11	Oklahoma	114,553
12	New Jersey	114,472
13	Arkansas	113,225
14	Delaware	111,845
15	Indiana	111,079
16	Alabama	111,070
17	Illinois	109,491
18	Kansas	108,567
19	Florida	108,390
20	New York	108,027
21	Idaho	107,747
22	Mississippi	106,801
23	Minnesota	106,698
24	Nevada	105,410
25	Montana	104,894
26	Wyoming	104,418
27	Georgia	103,592
28	Kentucky	103,127
29	Massachusetts	102,613
30	Texas	101,897
31	Louisiana	101,597
32	Missouri	100,400
33	Michigan	99,299
34	Connecticut	97,517
35	New Mexico	96,918
36	California	95,965
37	North Carolina	95,747
38	Alaska	95,377
39	Colorado	95,008
40	Pennsylvania	94,370
41	Ohio	94,352
42	West Virginia	90,375
43	Virginia	79,173
44	Maryland	76,132
45	New Hampshire	72,655
46	District of Columbia	69,445
47	Washington	57,733
48	Puerto Rico	53,976
49	Maine	50,497
50	Oregon	47,911
51	Vermont	38,834
52	Hawaii	24,798

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Wyoming	166
2	West Virginia	126
3	Louisiana	116
4	Florida	111
5	Nevada	100
6	Washington	93
7	Idaho	87
8	Alabama	81
9	North Carolina	73
10	Colorado	72
11	Alaska	66
12	Oregon	64
13	Montana	63
14	New Mexico	63
15	Arizona	58
16	District of Columbia	53
17	Kentucky	50
18	Mississippi	50
19	Missouri	50
20	Kansas	49
21	Michigan	49
22	North Dakota	48
23	Rhode Island	47
24	Utah	47
25	Illinois	46
26	Pennsylvania	44
27	Oklahoma	42
28	Tennessee	42
29	Wisconsin	42
30	Indiana	38
31	Arkansas	37
32	Texas	37
33	Maine	35
34	Connecticut	31
35	Delaware	30
36	Georgia	29
37	South Carolina	28
38	Ohio	27
39	Iowa	26
40	Hawaii	24
41	New York	24
42	South Dakota	24
43	New Jersey	23
44	California	21
45	Puerto Rico	20
46	Maryland	15
47	Massachusetts	15
48	Minnesota	15
49	New Hampshire	15
50	Virginia	15
51	Nebraska	13
52	Vermont	12

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,954
2	New York	2,713
3	Massachusetts	2,594
4	Rhode Island	2,560
5	Mississippi	2,460
6	Arizona	2,424
7	Connecticut	2,313
8	South Dakota	2,282
9	Louisiana	2,279
10	Alabama	2,277
11	Pennsylvania	2,133
12	Michigan	2,043
13	New Mexico	2,036
14	Indiana	2,025
15	North Dakota	2,024
16	Illinois	1,991
17	Arkansas	1,933
18	Iowa	1,921
19	Georgia	1,903
20	South Carolina	1,891
21	Oklahoma	1,848
22	Nevada	1,815
23	Tennessee	1,810
24	Texas	1,784
25	Kansas	1,755
26	Florida	1,719
27	Delaware	1,710
28	Ohio	1,704
29	District of Columbia	1,608
30	California	1,601
31	Kentucky	1,593
32	Maryland	1,591
33	Missouri	1,582
34	West Virginia	1,562
35	Montana	1,516
36	Wisconsin	1,355
37	Minnesota	1,333
38	Virginia	1,312
39	North Carolina	1,251
40	Wyoming	1,244
41	Nebraska	1,231
42	Idaho	1,174
43	Colorado	1,169
44	New Hampshire	995
45	Puerto Rico	785
46	Washington	767
47	Utah	718
48	Oregon	641
49	Maine	615
50	Alaska	481
51	Vermont	408
52	Hawaii	351

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kentucky	13
2	South Dakota	3
3	Wisconsin	3
4	Florida	2
5	Montana	2
6	Oklahoma	2
7	Alabama	1
8	Delaware	1
9	District of Columbia	1
10	Idaho	1
11	Illinois	1
12	Louisiana	1
13	Michigan	1
14	Mississippi	1
15	Missouri	1
16	New Jersey	1
17	Ohio	1
18	Oregon	1
19	Pennsylvania	1
20	Rhode Island	1
21	Tennessee	1
22	Virginia	1
23	Washington	1
24	West Virginia	1
25	Alaska	0
26	Arizona	0
27	Arkansas	0
28	California	0
29	Colorado	0
30	Connecticut	0
31	Georgia	0
32	Hawaii	0
33	Indiana	0
34	Iowa	0
35	Kansas	0
36	Maine	0
37	Maryland	0
38	Massachusetts	0
39	Minnesota	0
40	Nebraska	0
41	Nevada	0
42	New Hampshire	0
43	New Mexico	0
44	New York	0
45	North Carolina	0
46	North Dakota	0
47	Puerto Rico	0
48	South Carolina	0
49	Texas	0
50	Utah	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	392,225	1	99
Crowley	Colorado	363,966	2	99
Bent	Colorado	276,493	3	99
Dewey	South Dakota	250,339	4	99
Lincoln	Arkansas	246,314	5	99
Davidson	Tennessee	143,126	193	93
York	South Carolina	113,564	988	68
Richland	South Carolina	113,448	992	68
Orange	California	85,717	2264	27
Pierce	Washington	61,261	2835	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Hancock	Georgia	8,041	3	99
Jerauld	South Dakota	7,948	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,596	1855	40
Richland	South Carolina	1,373	2102	33
Davidson	Tennessee	1,371	2104	33
York	South Carolina	1,370	2108	32
Pierce	Washington	771	2728	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.